In the past few years, the ethical ramifications of AI technologies have been at the center of intense debates. Considerable attention has been devoted to understanding how a morally responsible practice of data science can be promoted and which values have to shape it. In this context, ethics and moral responsibility have been mainly conceptualized as compliance to widely shared principles. However, several scholars have highlighted the limitations of such a principled approach. Drawing from microethics and the virtue theory tradition, (...) in this paper, we formulate a different approach to ethics in data science which is based on a different conception of “being ethical” and, ultimately, of what it means to promote a morally responsible data science. First, we develop the idea that, rather than only compliance, ethical decision-making consists in using certain moral abilities, which are cultivated by practicing and exercising them in the data science process. An aspect of virtue development that we discuss here is moral attention, which is the ability of data scientists to identify the ethical relevance of their own technical decisions in data science activities. Next, by elaborating on the capability approach, we define a technical act as ethically relevant when it impacts one or more of the basic human capabilities of data subjects. Therefore, rather than “applying ethics”, data scientists should cultivate ethics as a form of reflection on how technical choices and ethical impacts shape one another. Finally, we show how this microethical framework concretely works, by dissecting the ethical dimension of the technical procedures involved in data understanding and preparation of electronic health records. (shrink)